Relevant System Properties Research Articles

Chiral active systems near a substrate: Emergent damping length controlled by fluid friction

Chiral active fluids show the emergence of a turbulent behaviour characterised by multiple dynamic vortices whose maximum size varies for each experimental system, depending on conditions not yet identified. We propose and develop an approach to model the effect of friction close to a surface in a particle based hydrodynamic simulation method in two dimensions, in which the friction coefficient can be related to the system parameters and to the emergence of a damping length. This length is system dependent, limits the size of the emergent vortices, and influences other relevant system properties such as the actuated velocity, rotational diffusion, or the cutoff of the energy spectra. Comparison of simulation and experimental results of a large ensemble of rotating colloids sedimented on a surface shows a good agreement, which demonstrates the predictive capabilities of the approach, which can be applied to a wider class of quasi-two-dimensional systems with friction.

Quantum advantage and stability to errors in analogue quantum simulators

Several quantum hardware platforms, while being unable to perform fully fault-tolerant quantum computation, can still be operated as analogue quantum simulators for addressing many-body problems. However, due to the presence of errors, it is not clear to what extent those devices can provide us with an advantage with respect to classical computers. In this work, we make progress on this problem for noisy analogue quantum simulators computing physically relevant properties of many-body systems both in equilibrium and undergoing dynamics. We first formulate a system-size independent notion of stability against extensive errors, which we prove for Gaussian fermion models, as well as for a restricted class of spin systems. Remarkably, for the Gaussian fermion models, our analysis shows the stability of critical models which have long-range correlations. Furthermore, we analyze how this stability may lead to a quantum advantage, for the problem of computing the thermodynamic limit of many-body models, in the presence of a constant error rate and without any explicit error correction.

The Jahn-Teller Effects in Chemical Reactions and Materials Science

In a semi-review paper (a review with novel results included), we note first that the so-called Jahn-Teller effects (JTEs), in their presently recognized four modifications, emerge as particular cases of a more general law, stating that “Nature tends to avoid degeneracies and pseudo-degeneracies in atomic matter by means of spontaneous symmetry breaking (SSB)”. This “Law of Nature” obviously influences all the relevant properties of polyatomic systems, including those with direct applications in materials science and engineering, as well as in materials transformations, notably, in chemical reactions. The exploration to the latter is quite novel: we show that the energy barriers in chemical transformations are controlled by the pseudo-JTE (PJTE), opening uncharted ways of catalytic action. The applications of JTEs theory in materials science is already more advanced, utilizing the SSB-induced special nuclear dynamics, which is very sensitive to external perturbations. One of its novel aspects is to modify the properties of the system by means of external influence targeting its JTEs parameters, their underlying degeneracies. We show here some notable trends with illustrative examples, including the JTEs in local centers in crystals used as qubits in quantum information storage, alteration of whole crystal lattice properties by inducing the JTE in one of its sublattices, planarization of puckered (buckled) two-dimensional systems by coordination to atoms that quench their PJTE-induced out-of-plane distortion, and the origin of a whole series of solid state properties of perovskite crystals, triggered by the local PJTE.

Inaugural Review Prize 2023: The exercise hyperpnoea dilemma: A 21st-century perspective.

During mild or moderate exercise, alveolar ventilation increases in direct proportion to metabolic rate, regulating arterial CO2 pressure near resting levels. Mechanisms giving rise to the hyperpnoea of exercise are unsettled despite over a century of investigation. In the past three decades, neuroscience has advanced tremendously, raising optimism that the 'exercise hyperpnoea dilemma' can finally be solved. In this review, new perspectives are offered in the hope of stimulating original ideas based on modern neuroscience methods and current understanding. We first describe the ventilatory control system and the challenge exercise places upon blood-gas regulation. We highlight relevant system properties, including feedforward, feedback and adaptive (i.e., plasticity) control of breathing. We then elaborate a seldom explored hypothesis that the exercise ventilatory response continuously adapts (learns and relearns) throughout life and ponder if the memory 'engram' encoding the feedforward exercise ventilatory stimulus could reside within the cerebellum. Our hypotheses are based on accumulating evidence supporting the cerebellum's role in motor learning and the numerous direct and indirect projections from deep cerebellar nuclei to brainstem respiratory neurons. We propose that cerebellar learning may be obligatory for the accurate and adjustable exercise hyperpnoea capable of tracking changes in life conditions/experiences, and that learning arises from specific cerebellar microcircuits that can be interrogated using powerful techniques such as optogenetics and chemogenetics. Although this review is speculative, we consider it essential to reframe our perspective if we are to solve the till-now intractable exercise hyperpnoea dilemma.

On Survival of Coherent Systems Subject to Random Shocks

We consider coherent systems subject to random shocks that can damage a random number of components of a system. Based on the distribution of the number of failed components, we discuss three models, namely, (i) a shock can damage any number of components (including zero) with the same probability, (ii) each shock damages, at least, one component, and (iii) a shock can damage, at most, one component. Shocks arrival times are modeled using three important counting processes, namely, the Poisson generalized gamma process, the Poisson phase-type process and the renewal process with matrix Mittag-Leffler distributed inter-arrival times. For the defined shock models, we discuss relevant reliability properties of coherent systems. An optimal replacement policy for repairable systems is considered as an application of the proposed modeling.

Ventilation Mechanics.

A fundamental task of the respiratory system is to operate as a mechanical gas pump ensuring that fresh air gets in close contact with the blood circulating through the lung capillaries to achieve O2 and CO2 exchange. To ventilate the lungs, the respiratory muscles provide the pressure required to overcome the viscoelastic mechanical load of the respiratory system. From a mechanical viewpoint, the most relevant respiratory system properties are the resistance of the airways (R aw), and the compliance of the lung tissue (C L) and chest wall (C CW). Both airflow and lung volume changes in spontaneous breathing and mechanical ventilation are determined by applying the fundamental mechanical laws to the relationships between the pressures inside the respiratory system (at the airway opening, alveolar, pleural, and muscular) and R aw, C L, and C CW. These relationships also are the basis of the different methods available to measure respiratory mechanics during spontaneous and artificial ventilation. Whereas a simple mechanical model (R aw, C L, and C CW) describes the basic understanding of ventilation mechanics, more complex concepts (nonlinearity, inhomogeneous ventilation, or viscoelasticity) should be employed to better describe and measure ventilation mechanics in patients.

Quasi-exact ground-state extrapolation for the random-field Potts model

The use of combinatorial optimization algorithms has contributed substantially to the major progress that has occurred in recent years in the understanding of the physics of disordered systems, such as the random-field Ising model. While for this system exact ground states can be computed efficiently in polynomial time, the related random-field Potts model is NP hard computationally. Hence, exact ground states cannot be computed for large systems in this case, but approximation schemes based on graph cuts and related techniques can be used. Here we show how a combination of such methods with repeated runs allows for a systematic extrapolation of relevant system properties to the ground state. The method is benchmarked on a special class of disorder samples for which exact ground states are available.

Network biology and artificial intelligence drive the understanding of the multidrug resistance phenotype in cancer.

Globally with over 10 million deaths per year, cancer is the most transversal disease across countries, cultures, and ethnicities, affecting both developed and developing regions. Tumorigenesis is dynamically altered by distinct events and can be lethal when untreated. Despite the innovative therapeutics available, multidrug resistance (MDR) to chemotherapy remains the major hindrance to the success of cancer therapy. The multiple mechanisms by which cancer cells evade cell death are diverse, indicating that MDR involves complex interconnected biological networks. Molecular profiling is currently able to stratify cancer into its distinct subtypes and help identify the best therapeutics, leading to "translational systems medicine". Highly specialized methodologies are generating a large amount of "omics" data - including epigenetics, genomics, transcriptomics, proteomics, metabolomics, as well as pharmacogenomics. Many of the resulting databases store data in non-standard formats, which need to be converted, interpreted, and merged into readable formats. The latest development of artificial intelligence (AI) methodologies and tools, coupled with advancements in large-scale data management and powerful graphic processing computing units, potentiate the integration of these large data sources into relevant biological networks, which will enhance our understanding of cancer MDR. In this review, we revisit common MDR mechanisms and compile a list of the most relevant "omics" public databases. We highlight examples of AI methods that are now decisively contributing to clear advances in cancer research, such as identification of new drugs from large databases and prediction of relevant drug, target, and system properties. An overview of several freely available "ready-to-use" algorithms is also provided. The described molecular scale AI algorithms and tools will undoubtedly guide important improvements in efficiency and efficacy of traditional methods of cancer diagnostics and treatment.

Modelling toolkit for simulation of maglev devices

A stand-alone App has been developed, focused on obtaining information about relevant engineering properties of magnetic levitation systems. Our modelling toolkit provides real time simulations of 2D magneto-mechanical quantities for superconductor (SC)/permanent magnet structures. The source code is open and may be customised for a variety of configurations. Ultimately, it relies on the variational statement of the critical state model for the superconducting component and has been verified against experimental data for YBaCuO/NdFeB assemblies. On a quantitative basis, the values of the arising forces, induced superconducting currents, as well as a plot of the magnetic field lines are displayed upon selection of an arbitrary trajectory of the magnet in the vicinity of the SC. The stability issues related to the cooling process, as well as the maximum attainable forces for a given material and geometry are immediately observed. Due to the complexity of the problem, a strategy based on cluster computing, database compression, and real-time post-processing on the device has been implemented.

Complexity analysis of simulations with analytic bond-order potentials

The modeling of materials at the atomistic level with interatomic potentials requires a reliable description of different bonding situations and relevant system properties. For this purpose, analytic bond-order potentials (BOPs) provide a systematic and robust approximation to density functional theory (DFT) and tight binding (TB) calculations at reasonable computational cost. This paper presents a formal analysis of the computational complexity of analytic BOP simulations, based on a detailed assessment of the most computationally intensive parts. Different implementation algorithms are presented alongside with optimizations for efficient numerical processing. The theoretical complexity study is complemented by systematic benchmarks of the scalability of the algorithms with increasing system size and accuracy level of the BOP approximation. Both approaches demonstrate that the computation of atomic forces in analytic BOPs can be performed with a similar scaling as the computation of atomic energies.

Monitoring Metric First-Order Temporal Properties

Runtime monitoring is a general approach to verifying system properties at runtime by comparing system events against a specification formalizing which event sequences are allowed. We present a runtime monitoring algorithm for a safety fragment of metric first-order temporal logic that overcomes the limitations of prior monitoring algorithms with respect to the expressiveness of their property specification languages. Our approach, based on automatic structures, allows the unrestricted use of negation, universal and existential quantification over infinite domains, and the arbitrary nesting of both past and bounded future operators. Furthermore, we show how to use and optimize our approach for the common case where structures consist of only finite relations, over possibly infinite domains. We also report on case studies from the domain of security and compliance in which we empirically evaluate the presented algorithms. Taken together, our results show that metric first-order temporal logic can serve as an effective specification language for expressing and monitoring a wide variety of practically relevant system properties.

Situated legal systems and their operational semantics

This work adopts H. Kelsen's concept of legal system, proposes a formal definition for such notion, and introduces an operational semantical framework for legal systems that are (structurally and operationally) situated in agent societies. Agent societies are defined. Relevant formal properties of situated legal systems (action-based dynamics; orthogonality between the operational semantics and the processes of legal reasoning and decision-making; validity of norms; and completeness) are discussed; the way they are exposed in the operational semantical framework is explained, and their truth formally proved. Also, for the sake of a better understanding of the legal-theoretic assumptions of the paper, recurring issues regarding Kelsen's theory of law (namely, his "positivism", the attribution of a plain deductive nature to legal reasoning and decision-making, and the notions of basic norm, authorization, and discretion) are briefly reviewed. They are put in confrontation with the points of view of R. Dworkin, H. Hart, and J. Raz, and an attempt is made to clarify them from the perspective of the provided formalization. A brief case study in agent-based modeling and simulation of public policy processes is presented, as an illustration of the way of using situated legal systems, and the proposed operational semantical framework, in a practical application.

Perspective: The Drawbacks in Using The Term ‘System of Systems’

Nancy Leveson, PhD, is a professor of aeronautics and astronautics and engineering systems at Massachussetts Institute of Technology, and is the author of several books on system concepts. E-mail: leveson.nancy8@gmail.com A few years ago, a new term, “system of systems,” was invented and has become quite popular. I’ve puzzled over this term because it doesn’t make any sense to me with respect to systems theory and systems engineering. Let’s start by reviewing some basic definitions in systems theory. A system can be defined as a set of components that act together as a whole to achieve some common goal, objective, or end. The components are all interrelated and are either directly or indirectly connected to each other. The system state at any point in time is the set of relevant properties describing the system at that time. The system environment is a set of components (and their properties) that are not part of the system, but whose behavior can affect the system state. The existence of a boundary between the system and its environment implicitly defines inputs or outputs as anything that crosses that boundary. It is important to understand that a system is always a model—an abstraction conceived by the viewer of the system. Systems and their boundaries do not exist in reality but only in the view of the beholder. One viewer may see a very different system than another in terms of where the boundaries are drawn, the relevant system properties and components, and even the purpose of the system. Abstractions are useful in that they help humans deal with complexity. One useful abstraction in understanding complex systems is to view them as hierarchical structures. A model of a complex system can be conceived in terms of a hierarchy of levels of organization, each more complex than the one below. Each level of the hierarchy can be thought of as a system, which is made up of components at a lower level. Each of these components (or subsystems) can itself be made up of subsystems, and so on. Figure 1 shows a depiction of a system labeled A (level 1 of the hierarchy) composed of three subsystems A1, A2, and A3 at level 2 of the hierarchy, each of which is made up of other components (level 3 of the hierarchy). Note that the term “system” is recursive in that a subsystem is itself a system, which is made

Collective dynamics of model microorganisms with chemotactic signaling

Various microorganisms use chemotaxis for signaling among individuals-a common strategy for communication that is responsible for the formation of microcolonies. We model the microorganisms as autochemotactic active random walkers and describe them by an appropriate Langevin dynamics. It consists of rotational diffusion of the walker's velocity direction and a deterministic torque that aligns the velocity direction along the gradient of a self-generated chemical field. To account for finite size, each microorganism is treated as a soft disk. Its velocity is modified when it overlaps with other walkers according to a linear force-velocity relation and a harmonic repulsion force. We analyze two-walker collisions by presenting typical trajectories and by determining a state diagram that distinguishes between free walker, metastable, and bounded cluster states. We mention an analogy to Kramer's escape problem. Finally, we investigate relevant properties of many-walker systems and describe characteristics of cluster formation in unbounded geometry and in confinement.

SPARE—A Scalable Algorithm for Passive, Structure Preserving, Parameter-Aware Model Order Reduction

This paper describes a flexible and efficient new algorithm for model order reduction of parameterized systems. The method is based on the reformulation of the parameterized system as a perturbation-like parallel interconnection of the nominal transfer function and the nonparameterized transfer function sensitivities with respect to the parameter variations. Such a formulation reveals an explicit dependence on each parameter which is exploited by reducing each component system independently via a standard nonparameterized structure preserving algorithm. Therefore, the resulting smaller size interconnected system retains the structure of the original system with respect to parameter dependence. This allows for better accuracy control, enabling independent adaptive order determination with respect to each parameter and adding flexibility in simulation environments. It is shown that the method is efficiently scalable and preserves relevant system properties such as passivity. The new technique can handle fairly large parameter variations on systems whose outputs exhibit smooth dependence on the parameters, also allowing design space exploration to some degree. Several examples show that besides the added flexibility and control, when compared with competing algorithms, the proposed technique can, in some cases, produce smaller reduced models with potential accuracy gains.

Polynomial wavelets in hybrid‐mixed stress finite element models

AbstractThis paper reports the use of polynomial wavelets as approximation functions in hybrid‐mixed stress finite element models applied to the solution of plane elasticity problems. The stress and displacement fields in the domain and the displacements on the static boundary are independently approximated. The kinematic boundary conditions are locally satisfied. All remaining equations are enforced in a weighted residual form so designed as to ensure that the discrete model embodies the relevant properties of continuum systems, namely the static‐kinematic duality and elastic reciprocity. A set of numerical applications is presented to illustrate the use of the hybrid‐mixed model and to assess its efficiency and accuracy. Copyright © 2009 John Wiley & Sons, Ltd.

Risk Mapping for Avian Influenza: a Social–Ecological Problem

Pathogen dynamics are inseparable from the broader environmental context in which pathogens occur. Although some pathogens of people are primarily limited to the human population, occurrences of zoonoses and vector-borne diseases are intimately linked to ecosystems. The emergence of these diseases is currently being driven by a variety of influences that include, among other things, changes in the human population, long-distance travel, high-intensity animal-production systems, and anthropogenic modification of ecosystems. Anthropogenic impacts on ecosystems have both direct and indirect (food- web mediated) effects. Therefore, understanding disease risk for zoonoses is a social-ecological problem. The articles in this special feature focus on risk assessment for avian influenza. They include analyses of the history and epidemiological context of avian influenza; planning and policy issues relating to risk; the roles of biogeography and spatial and temporal variation in driving the movements of potential avian influenza carriers; approaches to quantifying risk; and an assessment of risk-related interactions among people and birds in Vietnamese markets. They differ from the majority of published studies of avian influenza in that they emphasize unknowns and uncertainties in risk mapping and societal responses to avian influenza, rather than concentrating on known or proven facts. From a systems perspective, the different aspects of social-ecological systems that are relevant to the problem of risk mapping can be summarized under the general categories of structural, spatial, and temporal components. I present some examples of relevant system properties, as suggested by this framework, and argue that, ultimately, risk mapping for infectious disease will need to develop a more holistic perspective that includes explicit consideration of the roles of policy, disease management, and feedbacks between ecosystems and societies.

Entanglement and magnetic order

In recent years quantum statistical mechanics have benefited of cultural interchanges with quantum information science. There is much evidence that quantifying the entanglement allows a fine analysis of many relevant properties of many-body quantum systems. Here we review the relation between entanglement and the various types of magnetic order occurring in interacting spin systems.

Sequential estimation for prescribed statistical accuracy in stochastic simulation of biological systems

Stochastic simulation of biological systems proceeds by repeatedly generating sample paths or trajectories of the underlying stochastic process, from which many relevant and important system properties can be obtained. While a great deal of research is targeted towards accelerated trajectory generation, issues concerned with the variability across trajectories are often neglected. Advanced methods for properly quantifying the statistical accuracy and determining a reasonable number of trajectories are hardly addressed formally in the context of biological system simulation, though mathematical statistics provides a large body of powerful theory. We invoke this theory and show how mathematically well-founded sequential estimation approaches serve for systematically generating enough but not too many trajectories for achieving a certain prescribed accuracy. The practical applicability is demonstrated and illustrated by numerical examples through simulation studies of an immigration-death process and a gene regulatory network.

A role for M -matrices in modelling population growth

Adopting a discrete-time cohort-type model to represent the dynamics of a population, the problem of achieving a desired total size of the population under a balanced growth (contraction) and the problem of maintaining the desired size, once achieved, are studied. Properties of positive-time systems and M-matrices are used to develop the results, which are illustrated using simple examples. The material is presented in a format that makes it appropriate as background material for interesting applications based modelling assignments on undergraduate programmes in the mathematical sciences, applied sciences, economics and technology. All relevant properties of both positive-time systems and M-matrices are presented and clarified at the outset.